Author: balajipanigrahy

With over 17 years of experience, Balaji has gained expertise in various facets of business consultation, project management, service delivery in SAP implementation and diversified support across varied industries, like Airport, Transportation, Infrastructure, Power Plant, Hospital, Educational Institution, Hotels, and Shared Service Centers. With this blog, he shares his expertise and insights on a few of the many things that he has learnt at work.

Big data analytics is the process of studying varied data sets to reveal hidden patterns, customer behaviours, market trends and unknown correlations to help organizations make more informed business decisions.

The emergence of big data has allowed organizations to put scattered data into use and extract meaningful insights from it. Much to everyone’s amazement, the data which was left unrecognized or considered useless in the past has suddenly become a goldmine for organizations across industries.

Big data analytics is important to health care. By analyzing large amounts of information – both structured and unstructured – health care providers can provide lifesaving diagnoses or treatment options almost immediately. This is helpful especially in Oncology.

Similarly, people evading tax can be caught through big data analytics. By gathering data from various source and plotting against income with expenditure, a plot can be find out.

Big data helps in five different ways:

A) Social listening

This gives you the power to determine who is saying what about your business. Brand sentiment analysis presents the type of detailed feedback you can’t get from regular polls or surveys.

B) Cost reduction

Thanks to big data technologies such as Hadoop and cloud-based analytics, it is possible to reduce the cost when it to storing large amounts of data, along with identifying more efficient ways of doing business.

C) Comparative analysis

Big data allows the comparison between products, services and overall brand authority with the competition by cross examining user behavior metrics and seeing how consumers are engaging with businesses in real-time.

D) Marketing analytics

The information gained from marketing analytics helps in promotion of new products or services in a more informed, innovative way.

E) Niche targeting

This stream of big data offers the power to dig into social media activity about a particular subject from multiple sources in real-time, identifying audiences for marketing campaigns.

If your business is clueless about using big data, here are six tips you can apply to ensure success:

1) Identify your KPIs

Big data is all about the big picture. Small data drills down to uncover specific actions that lead to improved results. Start with identifying your KPIs and people responsible for each status and assigning them the task of tracking the development of a particular indicator.

2) Choose the right data

Data is everywhere. You can not only have access to real-time information but also get historical insights and predicts stats for the future. For organizations just starting with big data, this can be overwhelming. Therefore, it is important to source data creatively.

Build models that predict and optimize business outcomes. Your organization’s capabilities won’t transform until you collect and analyse those data sets that are actually relevant to your business growth. Don’t get overwhelmed with data.

3) Get actionable insights

Big data reports provide information on every department of the organization. The analysis throws light on every metric pre-defined by you. The outcomes can be quite interesting and inspire you to make quick strategic changes.

4) Foster collaboration and cooperation

Big data analytics delivers the outcome in easily digestible bytes – which are strategic and can be sent to key decision makers or employees responsible for a particular task. Your co-workers will be able to utilize those reports better by analyzing various data sources online, and collaborating effectively to carry on for the completion of the assigned task.

5) Gain predictive analytics

Your organization can make decisions based on the info that is likely to happen in the near future through big data analytics. You can use the right data visualizations for your data stories, and hence make better and faster business decisions.

Over to you

Don’t try to become a big data powerhouse overnight. Building data resources takes time and support from the right people. Therefore, hire people who specialize in big data analytics. Use the right technology to leverage big data. Think cloud. Good luck!

With information flowing in from a number of sources — websites, mobiles, social media, and other digital channels, organizations are swarmed with volumes of data today. But the question that continues to remain unanswered, is how businesses can make use of this data. The answer lies in data mining.

Let’s take a look at the five data mining techniques that can help businesses garner actionable insights from all the data.

1) Classification analysis: Data is classified into different sets in order to reach an accurate analysis or prediction. An example of application of classification analysis is when banks try to determine who should be offered a loan.

Applying classification analysis to the database, they can define the predictors — annual income, age etc. and the predictor attributes — numerical values corresponding to the predictors. Using IF/THEN analysis, they can then decide whether someone qualifies for the loan. For example, if the age is more than 20 years and income is equal to or more than Rs. 50000 per month, they qualify for the loan.

2) Association Rule Learning: By far the largest application of association rule learning has been in forecasting customer behavior. This is because the technique helps identify relationships between different variables and establish hidden patterns in the data. This data mining technique is widely used for analyzing sales transactions.

An example of association rule learning in an industry like online retail could be — A user who buys product ‘A’ and also product ‘B’, is likely to buy product ‘C’ for a consequent need.

3) Anomaly or Outlier Detection: This techniques digs into the outliers in a data set. Outliers/anomalies are patterns that do not match expected behavior. When an event that does not conform to a predefined pattern occurs, data analysts categorize this as noise and remove it from the remaining data set. Also, when outliers are detected analysts try to find out what caused the disturbance in the expected patterns. System health monitoring and fault detection are two applications of outlier detection.

4) Clustering Analysis: In this technique, data objects are grouped in clusters on the basis of similarity. The idea is to group data objects in such a manner that the degree of association is maximal within each cluster and minimal outside it. For instance, clusters of symptoms such as paranoia, schizophrenia etc. need to be correctly diagnosed in psychiatry, for the right therapy to be started.

5) Regression Analysis: In this type of analysis technique, there is a response variable and one or more than one predictor. The predictor variable(s) are independent and the responsive is dependent. The technique is used for studying how changing the value of predictor can alter the value of responsive variables. Note that only altering the predictor values can change the values for responsive, and this is not true vice-versa. Regression analysis is being used since long as a forecasting technique, and to study causal relationships.

In businesses, regression analysis can be used to predict events yet to occur. Insurance companies, for example, use regression analysis to find out how many people will be victims of theft.

Optimizing business processes is another application of regression analysis. For example, a company might want to understand and optimize the wait time of a customer call and the number of successful sales, to find out what should be the optimum wait time for a client call to be answered.

Each of the five discussed data mining techniques can help businesses gain valuable insights from data and use it solve tough business problems. Converting raw data into knowledge is the key to making better, smarter, and informed decisions.

The human resources departments are usually packed with activity. From understanding the resource needs of a business to connecting and finding the right

A typical human resources department is a frenzy of activities. From understanding the needs of a business to connecting with and finding the right resources to fulfil them, the department has a lot on their hands at any given point of time.

But today, a new world of HR technology and design teams is on the horizon of growth. Modern technologies like mobile, cloud computing and more are now enabling HR leader to not just revolutionize hiring, but also improve the employee experience.

How digitization will change HR

Employee experience

The current HR processes are mostly based on system records with web browser access, paper form to web form and transaction system based on process design of a business. But with digitization, HR will become an integrated platform using mobile apps, cloud based applications, real time analytics, employee dashboards and more, to create a human centered experience driven design.

Goal driven approach

The HR can now think of a way to make people and technology work towards achieving business goals in a more effective manner. By bringing together social, mobile, analytics and cloud technologies (SMAC), the HR can use the platform to develop apps that keep employees engaged and help them work towards the end goal, mitigating all the risks in between.

Efficient hiring

Digitization of HR will enable professionals to dig deeper into the talent pool from across the world. Using smarter apps and data analysing tools, the HR can look for specific skill sets in the desired fields and connect with them almost instantly. The modernization of the approach is sure to reduce the hiring cycle for businesses.

Employee satisfaction

The HR process – from hire to retire, plan to source, acquire to onboard, performance monitoring to rewarding employees, assessing productivity to developing effective strategies, will all be simplified with digitization. With modern technology the HR can dig deeper into employee analytics apart from general demographics to understand how they work, how they can be encouraged to perform better and what roles they are the best suited for.

Decentralisation of HR

With the latest technologies, businesses can expand their HR teams to multiple locations and access a global talent pool for hiring. The staff can work over a secure, uninterrupted and integrated platform from anywhere, anytime, making it easier to collaborate with the in-house team. With remote working becoming a trend amongst the millennials, digitization is a welcome change to HR.

How can businesses get started

By using modern technology, all processes can now be made on a workflow basis. This will ensure that employees can continue working in an effective manner, without having to reach out to the HR and awaiting a response.

While digitization will surely cut down on the role of the human resources department by automating redundant processes, businesses will still need professionals to manage some aspects of real employee engagement. Simply put, HR shared services and HR staff will need to be moved towards transformation.

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